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@CraigMorton
CraigMorton / brew_packages_full_reinstall.md
Last active April 15, 2026 22:25
brew full reinstall all packages

This forces a full reinstall of all homebrew packages, fixing the dyld: Library not loaded errors when trying to run git, psql, node etc. This often happens after using Apple's Migration Assistant. Also can be caused by macOS updates.

brew list -1 > brew.txt # list out all installed packages
brew list -1 | xargs brew rm --force # remove all installed packages
brew install $(cat brew.txt | tr '\n' ' ') # install all previously installed packages

source

Please summarize the paper. Follow these two steps.
## Step 1
Act as a curious, meticulous reader with attention to detail, objectivity, precision and sensitivity to novelty. Your job is to:
* Summarize each and every (!!!) key point/insight. Do not miss any; if there are many key points/insights, list them all regardless of length.
* Each point/insight must come with rich, precise, specific (!!!) details (e.g., numbers); details are really important.
* Each point/insight must be supported with direct quotes (!!!). Do not use quotes to simply repeat the point; instead, embed them naturally within your summary. Quotes should be used to better present the points (see the example below).
* If two points/insights are redundant, consider combining or integrating them. Be concise, but do not miss key points, insights, or details.
* If the text has sections (e.g., an academic paper), proceed section by section (e.g., focus on the first, then the second and so on), with each summary section starting with the origin

LLM Wiki

A pattern for building personal knowledge bases using LLMs.

This is an idea file, it is designed to be copy pasted to your own LLM Agent (e.g. OpenAI Codex, Claude Code, OpenCode / Pi, or etc.). Its goal is to communicate the high level idea, but your agent will build out the specifics in collaboration with you.

The core idea

Most people's experience with LLMs and documents looks like RAG: you upload a collection of files, the LLM retrieves relevant chunks at query time, and generates an answer. This works, but the LLM is rediscovering knowledge from scratch on every question. There's no accumulation. Ask a subtle question that requires synthesizing five documents, and the LLM has to find and piece together the relevant fragments every time. Nothing is built up. NotebookLM, ChatGPT file uploads, and most RAG systems work this way.

@Davc0m
Davc0m / shelly_pro3em_net_metering.js
Last active April 15, 2026 22:20
Shelly Pro 3EM: Saldierende Energiemessung (Net Metering) mit Home Assistant Auto-Discovery
/**
* Shelly Pro 3EM - Net Metering (Saldierung) & Home Assistant Auto-Discovery
* Version: 1.1.8
*
* DISCLAIMER:
* Use this script entirely at your own risk! I assume absolutely no liability
* for any direct, indirect, or consequential damages. This includes, but is
* not limited to, damage to the Shelly device, any connected electrical
* equipment, other devices in your network, data loss, or system malfunctions.
* By using this script, you acknowledge that you alone are responsible for
@st4rdog
st4rdog / msi-afterburner-undervolt-how-to.md
Last active April 15, 2026 22:18
MSI Afterburner Voltage Curve Editor Tutorial - Undervolt

Tutorial

  • Start at default curve.
  • Ctrl-click-drag any point in right-half to bend it until a point intersects with desired mhz/voltage.
  • Select point and adjust to perfection using shift-up-down.
  • Shift-click-drag empty space and select points (including selected point) on the right. Selected point should be left-most point.
  • Shift-Enter twice to flatten all points in selection area. They will flatten to match the selected point.
  • Adjust if required.

Controls

@rohitg00
rohitg00 / llm-wiki.md
Last active April 15, 2026 22:18 — forked from karpathy/llm-wiki.md
LLM Wiki v2 — extending Karpathy's LLM Wiki pattern with lessons from building agentmemory

LLM Wiki v2

A pattern for building personal knowledge bases using LLMs. Extended with lessons from building agentmemory, a persistent memory engine for AI coding agents.

This builds on Andrej Karpathy's original LLM Wiki idea file. Everything in the original still applies. This document adds what we learned running the pattern in production: what breaks at scale, what's missing, and what separates a wiki that stays useful from one that rots.

What the original gets right

The core insight is correct: stop re-deriving, start compiling. RAG retrieves and forgets. A wiki accumulates and compounds. The three-layer architecture (raw sources, wiki, schema) works. The operations (ingest, query, lint) cover the basics. If you haven't read the original, start there.

@Informatic
Informatic / domain.xml
Last active April 15, 2026 22:13
How to use Windows 10 OEM license in libvirt VM (<smbios mode='host' /> does not work as Windows seems to verify UUID; apparmor/security configuration changes may be needed)
<domain type='kvm' xmlns:qemu='http://libvirt.org/schemas/domain/qemu/1.0'>
<!-- ... -->
<qemu:commandline>
<qemu:arg value='-acpitable'/>
<qemu:arg value='file=/some/path/slic.bin'/>
<qemu:arg value='-acpitable'/>
<qemu:arg value='file=/some/path/msdm.bin'/>
<qemu:arg value='-smbios'/>
<qemu:arg value='file=/some/path/smbios_type_0.bin'/>
<qemu:arg value='-smbios'/>